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Multi-GraspLLM: A Multimodal LLM for Multi-Hand Semantic Guided Grasp Generation

Haosheng Li, Weixin Mao, Weipeng Deng, Chenyu Meng, Haoqiang Fan, Tiancai Wang, Yoshie Osamu, Ping Tan, Hongan Wang, Xiaoming Deng

arXiv:2412.08468v3cs.ROcs.CV

TL;DR

Multi-hand semantic grasp generation lacks large-scale datasets with fine-grained contact descriptions, despite the need to produce feasible, language-appropriate poses for different hands. The paper introduces Multi-GraspSet and a unified LLM-based generator, reporting state-of-the-art grasp generation and better joint-training results than separate per-hand models.

  • Problem

    Multi-hand grasp generation lacks large-scale datasets with fine-grained contact descriptions, while existing approaches often separate hands or omit semantic guidance.

    Source: §1 · pp. 1–2

  • Method

    The paper builds Multi-GraspSet and uses a unified LLM-based model to map point clouds and language instructions to hand-specific grasp angles.

    Source: §1 · p. 2

  • Results

    Multi-GraspLLM achieves state-of-the-art grasp generation, with joint multi-hand training outperforming separate per-hand models.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Multi-GraspSet and Multi-GraspLLM address the cross-hand semantic grasp-generation gap identified by the paper.

    Source: §6 · p. 9

Abstract

from arXiv · show

Multi-hand semantic grasp generation aims to generate feasible and semantically appropriate grasp poses for different robotic hands based on natural language instructions. Although the task is highly valuable, due to the lack of multihand grasp datasets with fine-grained contact description between robotic hands and objects, it is still a long-standing difficult task. In this paper, we present Multi-GraspSet, the first large-scale multi-hand grasp dataset with automatically contact annotations. Based on Multi-GraspSet, we propose Multi-GraspLLM, a unified language-guided grasp generation framework, which leverages large language models (LLM) to handle variable-length sequences, generating grasp poses for diverse robotic hands in a single unified architecture. Multi-GraspLLM first aligns the encoded point cloud features and text features into a unified semantic space. It then generates grasp bin tokens that are subsequently converted into grasp pose for each robotic hand via hand-aware linear mapping. The experimental results demonstrate that our approach significantly outperforms existing methods in both real-world experiments and simulator. More information can be found on our project page https://multi-graspllm.github.io.

1. Introduction

The paper addresses the lack of multi-hand grasp generation that combines cross-hand support with semantic guidance. It introduces a contact-annotated dataset and a unified LLM-based generator, reporting improved results over existing methods and separate per-hand training.

1. Introduction

  • Existing methods often train separate models for individual hands, while multi-hand semantic generation must map the same instruction to behaviors suited to different hand structures.The paper frames this as a dataset challenge and a unified-model challenge.Source: §1 · pp. 1–2
  • Multi-GraspSet combines physically stable multi-hand grasps, detailed contact annotations, and nearly 1.2M LLM-generated dialogue samples.The dataset is presented as a response to the shortage of multi-hand datasets with fine-grained contact descriptions.Source: §1 · p. 2
  • Multi-GraspLLM takes an object point cloud and text instruction and generates grasp angles for different hands through a hand-aware mapping.The unified model uses LLMs to handle variable-length inputs and outputs.Source: §1 · p. 2
  • Multi-GraspLLM achieves state-of-the-art grasp generation, and joint multi-hand training outperforms separate per-hand models.The paper reports improvements in pose accuracy and grasp quality in simulator and real-world experiments.Source: §1 · p. 2

2. Related Works

Related work covers grasp datasets, language-guided grasp generation, and multimodal LLMs in robotics. The reviewed literature includes physically stable and contact-aware datasets, language-conditioned methods with restricted scope, and multimodal systems using visual or point-cloud inputs.

2.1. Robotics Grasp Datasets

  • Existing grasp datasets emphasize physical stability or contact awareness; retargeting-based contact-aware datasets typically support robotic hands resembling human hands [52].Some physically stable multi-hand datasets support fewer objects.Source: §2.1 · p. 2

2.2. Language-guided Grasp Generation

  • Language-guided grasp methods target functional object parts, but reviewed approaches include methods without open-vocabulary support and methods limited to a single hand [18].Other systems use language-conditioned diffusion or large language models to score or generate grasps.Source: §2.2 · pp. 2–3

2.3. Multimodal LLMs in Robotics

  • Multimodal robotics work uses vision-language models to identify grasp regions and 3D point-cloud encoders for geometric features; SemGrasp displays human-hand poses from object point clouds [18].The reviewed work also includes language- and vision-conditioned discrete-action models.Source: §2.3 · p. 3

3. Multi-GraspSet Dataset

Multi-GraspSet is presented as a large-scale, multi-hand dataset with semantic contact annotations and grasp-focused conversations. Its construction combines unified grasp generation, SDF-based contact labeling, and LLM-assisted conversation generation.

3. Multi-GraspSet Dataset

  • Multi-GraspSet is presented as the first large-scale multi-hand grasp dataset with semantic contact annotations.The paper describes it as created through unified grasp generation and LLM assistance.Source: §3 · p. 3

3.1. Dataset Statistics

  • The dataset covers five robotic hands, 2.1k object point clouds, 140k grasp pose pairs, and more than 1M conversations.Annotations include per-finger object contacts and a more general form when participating fingers contact the same object part; grasp poses also have 5–10 hand-aware question-answer conversations at different control levels.Source: §3.1 · pp. 3–4, Figure 3 · p. 4

3.2. Dataset Construction

  • Construction combines physically stable grasp generation, SDF-based contact annotation, and LLM-assisted conversation generation.The SDF procedure labels a hand link as contacting an object when its value falls below a threshold; contact descriptions can specify individual fingers or summarize shared contacts.Source: §3.2 · p. 4, §3.2.2 · p. 4

3.2.1. Unified Grasp Generation

  • Unified grasp generation uses DexGraspNet and GraspIt for four dexterous hands and Contact-GraspNet for the Panda Gripper.The grasp poses are generated from 2,100 meshes collected from OakInk and ShapeNet.Source: §3.2.1 · p. 4

3.2.2. Annotation for Contact Information

  • Contact annotation uses SDF values and object-face category labels to identify contacting links and the object parts they touch.The dataset provides both finger-specific contact descriptions and a readable summary when multiple fingers contact the same part.Source: §3.2.2 · p. 4

3.2.3. Annotation for Basic Conversation

  • Basic conversations cover three instruction levels, from object-and-hand questions to prompts specifying grasp parts and contact details.GPT-4o refines fixed-template sentences to improve phrasing variety and naturalness.Source: §3.2.3 · pp. 4–5

4. Method

Multi-GraspLLM turns object geometry and language instructions into hand-specific grasp poses through a unified LLM pipeline. Its method discretizes grasp angles, structures training as multimodal conversations, and uses two-stage tuning.

4.1. Multi-GraspLLM

  • Each hand's continuous grasp angles are discretized into hand-specific bins, then mapped back to wrist pose and hand-specific joint angles.The wrist pose is represented by [T; R] and joint angles by θ.Source: §4.1 · pp. 5–6
  • The framework encodes object geometry, aligns point-cloud tokens with language features, and feeds both to an autoregressive LLM that emits grasp-bin tokens.A de-tokenizer converts the generated sequence into grasp bins.Source: §4.1 · pp. 5–6

4.2. Data Format

  • Training data uses hand, scale, and grasp-bin tokens across single-round and two multi-round dialogue formats, with captioning added to strengthen geometric understanding.Multi-round formats include object-description-then-grasp interactions and more diverse dialogues involving different hands.Source: §4.2 · p. 6

4.3. Training Strategy

  • Training proceeds in two stages: multimodal alignment tunes only the adaptor, then instruction tuning updates the adaptor and LLM while freezing the point-cloud encoder.The first stage uses single-round grasp-generation conversations; the second uses all conversation types.Source: §4.3 · p. 6

5. Experiments

Experiments compare Multi-GraspLLM with existing methods and examine dataset metrics, implementation choices, and model ablations. Mixed training outperforms separate training and the reported baselines; ablations also identify gains associated with training strategy, tokens, and instruction data.

5.1. Dataset and Evaluation Metrics

  • The test point clouds are separate from those used for training and validation, and grasp intention and physical stability are evaluated separately.Chamfer distance measures intention alignment; maximum penetration and simulator success rate assess grasp quality.Source: §5.1 · p. 6

5.2. Implementation Details

  • Training uses multimodal alignment followed by instruction tuning, with 8,192 sampled points per object and grasp angles discretized into 384 bins.The two stages each run for three epochs, and bin bounds are determined separately for each hand from its dataset.Source: §5.2 · p. 7

5.3. Comparison with State-of-the-Art Methods

  • Mixed training outperforms separate training by a large margin, and both Multi-GraspLLM variants outperform the existing baselines, especially in intention accuracy.The comparison evaluates dexterous hands and parallel grippers separately; the table reports averages across hands.Source: §5.3 · p. 7, Table 2 · p. 7
  • The model generates grasps at low, mid, and high contact-information levels, from no contact data to object-part information and finger-contact control.The figure illustrates the three levels as a progression in contact detail.Source: §5.3 · p. 7, Figure 6 · p. 8

5.4. Ablation Study

  • Two-stage training and grasp-bin tokens significantly improve performance; scale tokens help compensate for PointBERT's loss of overall point-cloud scale.The passage also reports an impact from robotic-hand tokens, though the supplied text does not specify its magnitude.Source: §5.4 · p. 7, Table 3 · p. 7
  • Larger grasp-bin counts increase resolution but make prediction harder, while smaller counts simplify prediction at the cost of precision.The authors selected a moderate bin width to balance the trade-off; all tested configurations outperform the existing methods.Source: Table 4 · p. 8
  • Instruction tuning on increasingly complex, multi-round conversations yields consistently better performance than the simpler data structures.The authors attribute benefits to multitask learning and auxiliary captioning, which may reduce overfitting and keep geometric features in play.Source: §5.4 · p. 8, Table 5 · p. 8

6. Conclusion

The conclusion presents Multi-GraspSet as a multi-hand dataset with semantic guidance and Multi-GraspLLM as a language-guided method for generating grasps across robotic hands.

6. Conclusion

  • Multi-GraspSet is presented as the first multi-hand grasp dataset with rich semantic guidance.The authors describe Multi-GraspLLM as built on this dataset.Source: §6 · p. 8
  • Multi-GraspLLM generates grasp poses for different robotic hands from linguistic grasp descriptions, addressing a gap in cross-hand semantic grasp generation.The authors plan to collect more data using additional hand and object types to further enhance real-world generalization.Source: §6 · pp. 8–9
  • The authors identify larger data collections across more hand and object types as future work.They state this could further enhance generalization to real-world scenarios.Source: §6 · p. 9

A.1. Dataset Annotation

The appendix describes LLM-assisted dialogue annotation, the collection and sampling of grasp data, and the model's conversation-based training setup.

A.1. Dataset Annotation

  • GPT-4-assisted annotation uses generated templates, fills them with grasp-contact information, and refines the resulting dialogues for clarity and fluency.The process begins with manually crafted prompts expanded by GPT-4.Source: §A.1 · p. 12

A.2. Collection of Multi-GraspSet

  • The collection uses 1,800 Oakink objects and 300 ShapeNet objects, filters grasps exceeding 2 cm penetration, and samples grasp poses differently for dexterous hands and grippers.It selects one pose per contact pattern for dexterous hands and multiple poses per pattern for grippers.Source: §A.2 · p. 12

A.3. Multi-GraspLLM

  • The training data comprises 500k conversations for multimodal alignment and 300k for instruction tuning, using PointBERT and full-parameter tuning of the LLM and adaptor.Training uses eight 80G A100 GPUs; testing uses one A100 GPU.Source: §A.3 · p. 12

A.4. Evaluation Metrics

The appendix specifies metrics for grasp intention and physical stability, then reports how grasp intention varies across instruction levels while quality metrics remain consistent.

A.4. Evaluation Metrics

  • Chamfer distance evaluates grasp intention, while maximum penetration distance and grasp success rate evaluate physical stability and grasp quality.The appendix calculates penetration using point-to-surface distance for uniformly sampled hand points.Source: §A.4 · p. 12

B. Additional Ablation Experiments

B.1. Effect of Instruction Levels

  • Multi-GraspLLM has the lowest Chamfer distance under high-level instructions with detailed contact information, while grasp-quality metrics stay consistent across levels.The model has the highest Chamfer distance at the low level and intermediate values at the mid level; the baseline's performance is consistent across levels.Source: §B.1 · p. 13, Figure 7 · p. 13

B.2. Effect of Training Data Amount for Modality Alignment

The section examines how training-data amount and structure affect modality alignment and instruction tuning; more alignment data improves intention understanding and grasp quality.

B.2. Effect of Training Data Amount for Modality Alignment

  • More modality-alignment data improves intention understanding, measured by lower CD, and grasp quality, measured by higher Suc.The section also analyzes instruction-tuning data structure, but the supplied passage gives no result for that analysis.Source: §B.2 · p. 13
  • Table 9 reports Allegro performance under training with different numbers of hands.The supplied caption identifies the table’s scope but gives no performance values.Source: Table 9 · p. 13

B.3. Effect of Used Hands in Training

The section reports that mixed training across hands benefits Allegro performance, then describes real-world systems and their evaluation on everyday objects.

B.3. Effect of Used Hands in Training

  • Starting from three training hands, mixed training outperforms separate training, and performance improves as the number of hands and data volume grow.The evaluated sets add Panda, Barrett, and Jaco to Allegro and Shadow.Source: §B.3 · p. 13

C. Real World Experiments

C.1. Robot System

  • Real-world evaluation used gripper and Allegro-hand systems, testing 29 daily-life objects with 20 trials per method and manual success and part-selection assessment.Tabletop-colliding poses were filtered out; the supplied table caption identifies Table 10 as the real-world results table.Source: §C.1 · p. 14, Table 10 · p. 14

C.2. Experiments Results

In real-world tests, Multi-GraspLLM achieves comparable grasp success but significantly better part-selection accuracy than the compared methods.

C.2. Experiments Results

  • Multi-GraspLLM achieves comparable real-world grasp success rates but significantly higher part-selection accuracy than traditional grasp-quality methods and DexGYS [52].Part-selection accuracy measures grasp intention; Table 10 presents the real-world results.Source: §C.2 · p. 14
  • Figure 9 illustrates grasping experiments under different grasp descriptions.The supplied caption identifies it as an illustration of the real-world grasp process.Source: Figure 9 · p. 15
  • Figure 10 provides additional visualizations of grasp poses generated by Multi-GraspLLM.Source: Figure 10 · p. 16
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